The three-line hybrid rice system, which relies on cytoplasmic male sterility (CMS), faces challenges in breeding efficiency. To address these challenges, we developed CMS engineering breeding technology (CEBT). First, we constructed a complementation vector containing the fertility restoration module Double Rf4-Rf20, sorting marker gene DsRed2, and pollen lethal gene ZmAA1, then transformed this into a CMS line. We obtained reproductive lines without alterations to main agronomic traits, which can produce engineering sterile lines (ESL) and reproductive line seeds at a 1:1 ratio by self-crossing. CEBT converts the traditional three-line system into an efficient ‘new two-line’ hybrid framework: the reproductive line is improved while simultaneously improving the ESL, which significantly shortens the breeding process for three-line hybrid rice.

High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).